UNDERSTANDING THE EFFECTS OF CONSUMERS’ VALUE-

TECHNOLOGY FIT ON A MOBILE SHOPPING WEBSITE：THE

CASE OF RAKUTEN ICHIBA

Wen-Lung Shiau, Department of Information Management, Ming Chuan University, Taoyuan,

Taiwan, R.O.C., mac@mail.mcu.edu.tw

Tsai-Ru Liou, Department of Information Management, Ming Chuan University, Taoyuan,

Taiwan, R.O.C., agnw320@hotmail.com

Abstract

Mobile shopping is very popular nowadays. Many mobile shopping websites were founded in recent

years, such as Yahoo! and Rakuten Ichiba. Most previous articles focused on theory of planned

behavior, trust, flow, perceived usefulness, consumer usability preference, consumer shopping

experience and decision-making, integrating technology acceptance model and perceived value. There

are few studies to discuss benefits of matching discounts/bargains with a mobile shopping website, as

well as how much affective reaction and flow affect continuance intention of using a mobile shopping

website. The aim of this study investigates the factors (value-technology fit, affective reaction, flow)

influencing continuance intention of the mobile shopping website, and mediation effects of affective

reaction and flow. The results of this study show that users’ value-technology fit significantly affects

their affective reaction, flow, and continuance intention to use the mobile shopping website. Affective

reaction and flow significantly affect users’ continuance intention to use the mobile shopping website.

Moreover, affective reaction and flow partially mediate relationship between value-technology fit and

users’ continuance intention to use the mobile shopping website, Rakuten Ichiba. The research

findings have suggestions for the mobile shopping managers and future research studies.

Keywords: Mobile Shopping, Value-Technology fit, affective reaction, flow, mediation effect.

1. INTRODICTION

The number of mobile devices accessing the Internet worldwide surpasses one billion in 2013. The

explosion in Internet-enabled devices coincides with an dramatic increase in mobile Internet users

worldwide (Gonsalves, 2009). A recent research on mobile service demonstrates that 57.1% of

consumers have had the experience of shopping on mobile devices (Market Intelligence and

Consulting Institute, 2013). According to a research study done by Allied Business Intelligence, Inc.

company (ABI), the amount of global mobile shopping sales will exceed 16.3 billion US dollars,

which is 12% at global mobile shopping sales. eMarketer research estimated the amount of mobile

shopping will be 87 billion US dollars in 2016, and mobile commerce will increase noticeably (Musil,

2013). Thus, the mobile shopping has become more and more popular worldwide.

According to Ipsos, a global market research company, the population of using smartphones has

reached more than 51% in Taiwan from 2011 to 2013. A survey of Taiwan Network Information

Center (TWNIC) showed that 17.53 million people used network in Taiwan in 2012, and the

penetration rate of using wireless network is 75.44%. In addition, more than 44% of people in Taiwan

use smartphone or tablet personal computer (Taiwan Network Information Center, 2012). Mobile

internet has emerged in people’s life because more and more people buy goods on mobile devices,

especially smartphones. This type of commerce is called “Mobile Commerce (MC)”. Mobile

Commerce refers to users carry out a transaction or search information through wireless devices. MC

always has the features of interactivity, process, displaying information, and producing new

capabilities such as location awareness, context sensing, and push delivery. Therefore, these features

have aroused novel services, such as location-based service, and context-aware service. These

characteristics of MC raised concerns for both academic and industry. MC has been an inevitable

mainstream issue.

Previous mobile shopping studies focus more on theory of planned behavior (Yang, 2012), trust, flow,

perceived usefulness (Zhou, 2013), consumer usability preferences (Ozok & Wei, 2010), consumer

shopping experience and decision-making (Karaatli et al., 2010), integrating technology acceptance

model and perceived value (Ko et al., 2009). Another research stream is fit. Previous fitness studies

focus on person-environment (P-E) fit, such as Hu et al. (2007) examined applicant attraction to an

organization in the context of web-based recruitment, and Okazaki and Yagüe (2012) investigated the

effects of an advergame on perceived brand value in a context of mobile social networking sites.

However, few studies examined the effects of value-technology fit, or the degree of matching between

discounts/bargains with a mobile shopping website on continuance intention to use the mobile

shopping website. Thus, the purpose of this study is to understand the factors (value-technology fit,

affective reaction, flow) influencing continuance intention of the mobile shopping website of Rakuten

Ichiba in Taiwan. The questions addressed in this paper are: (a) will value-technology fit influence

continuance intention of the mobile shopping website directly? (b) will value-technology fit influence

continuance intention of the mobile shopping website through affective reaction and flow?

The remainders of this paper are structured as follows. Section 2 describes mobile shopping, fit,

affective reaction and flow. Section 3 presents the research model and hypothesis. Section 4 describes

the research case and methodology. Section 5 presents the results of data analysis. Section 6 discusses

the main findings and presents the conclusion. The final section presents the research limitations.

2 LITERATURE REVIEW

2.1 Mobile Commerce and Mobile Shopping

When wireless network and mobile devices usage dramatically, e-commerce develops a new

commercial type, mobile shopping. Although characteristic of electronic commerce is similar to

mobile commerce, electronic commerce emphasizes to conduct transactions via personal computer;

however, mobile commerce emphasizes to conduct transactions and services via wireless devices such

as mobile phone (Turban et al., 2008).Since mobile devices appeared, more and more research

explores about mobile commerce. Ngai and Gunasekaran (2007) reviewed 149 articles about mobile

commerce, and classification five levels as follows: (1) mobile commerce theory and research; (2)

wireless network infrastructure; (3) mobile middleware; (4) wireless user infrastructure; (5) mobile

commerce applications and cases. Moreover, many mobile services emerge such as communication

(e.g., text messages, e-mail, chat), locator and information services (e.g., store locator), commerce

transactions (e.g., purchases, reservations, shopping assistance, and financial services), and content

(e.g., news, updates, entertainment services) (Karaatli et al., 2010). One of remarkable mobile services

is mobile shopping. Some researchers proposed different definitions in mobile shopping as shown in

the following. Kumar and Mukherjee (2013) defined mobile shopping is a technology process that

involves an active interaction between user and technology. Ko et al. (2009) defined mobile shopping

as the activities of consumers who use wireless Internet service when shopping and purchasing via a

mobile phone. Yang (2012) showed that mobile shopping is presented in a technology-mediated

environment and connected via personalized mobile devices. In sum, mobile shopping is still growing

up and will become an important trend in the future.

2.2 Fit

Fit shows different types of match in social science. For example, fit is to connect with individual’s

department, colleague, or environment and performance in the work. If they match pretty well,

performance will better (Jr. et al., 1993). Person-Environment (P-E) fit was been proposed from Plato

whom observed individual would search jobs that matching with oneself (Kaplan, 1950). P-E fit was

defined as the degree to which individual and environmental characteristics match (Kristof-Brown et

al., 2005). Pervin (1968) defined P-E fit as a fit between the personality characteristics of one

individual and those of another, or between an individual and the social climate created by a group of

individuals, leads to high performance.

The psychologists who researched organization behavior and industry organization had extremely

interest in fit which between individual and environment (Pervin, 1968). Pervin (1968) pointed out

that a "match" or "best-fit" of individual to environment is viewed as expressing itself in high

performance, satisfaction, and little stress in the system whereas a "lack of fit" is viewed as resulting in

decreased performance, dissatisfaction, and stress in the system. P-E fit was been used of explaining a

stress about jobs (Cooper et al., 2001). Jr. et al. (1993) showed at least four different general

orientations toward P-E fit: (1) fit between individual knowledge-skills-abilities and job requirements,

(2) fit between individual needs, organizational structures, and reinforcement system, (3) fit between

individual value orientations and organizational culture or value, and (4) fit between individual

personality and perceived organizational image or personality. The amount of recent research about P-

E fit is increasing, and it has been used for exploring several issues, such as LeRouge et al. (2006)

used fit theory to examine job satisfaction and organizational commitment. Hu et al. (2007) adopted fit

framework to examine applicant attraction to an organization in the context of Web-based

recruitment.Wu et al. (2007) used P-E fit to explore between Internet recruitment websites and

organizational attraction. Maurer and Cook (2011) examined its implications for the ability of

companies to e-recruit high quality job applicants. Okazaki and Yagüe (2012) developed perceived

brand-game fit to explore on the effects of an advergame on perceived brand value in a context of

mobile social networking sites (SNSs).

After blooming development, “environment” was adapted to different situations like as Person-job fit

(P-J fit), Person-group fit (P-G fit), Person-supervisor fit (P-S fit), Person-organization fit (P-O fit). In

addition, Task-technology fit (TTF) is the most popular P-E fit at field of information technology. For

example, Yang et al. (2013) investigated the effects of TTF on perceived IS (information system) use

and individual task performance. Aljukhadar et al. (2014) used TTF theory to examine the drivers and

consequences of successful task completion by a user in an online context. In recent years, more and

more researchers subdivide “task” into different situations. For instance, Lee et al. (2007) presented a

“Culture-technology Fit” framework to examine how the cultural characteristics of users affected their

post-adoption beliefs about the mobile Internet. Vaidya and Seetharaman (2011) attempted to bridge

the gap using a “Context-technology Fit” framework to understand the factors which influence the

sophistication of use and the variables underlying each of these macro level constructs. Lu and Yang

(2014) examined and compared the impact of task, social, and technology characteristics on users’

intentions in using SNS by integrating the TTF model and social capital theory (social-technology fit).

2.3 Affective Reaction and Flow

The term, affection, is conceived as an umbrella for a set of specific mental process including

emotions, moods, and (possibly) attitudes, and considered a general category for mental feeling

process (Bagozzi et al., 1999). Ping (2013) thought that affection is a critical factor in human decisions

and behaviors within many social contexts. Djamasbi and Strong (2008) thought that management

information system (MIS) research using affective constructs has primarily focused on affective

reactions toward the use of technology and not the affective state of users when they are introduced to

information technology (IT). The positive affective reactions, includes happiness, pride, and

satisfaction, whereas failure is linked to negative affect such as disgust, despair, frustration,

disappointment, anger, hostility, depression, and alienation. One’s mood will influence the level of

effort on a computer-related task. Research on people’s responses to computers, however, indicates

that their emotional reactions include both positive (e.g. excitement, pride, satisfaction) and negative

affect (e.g. feelings of isolation, frustration, alienation, and anxiousness) (Dusick, 1998) .

Csikszentmihalyi (1993) pointed out flow experience is a crucial psychological factor, which describes

people’s feelings concerning an activity. Flow has been described as a state of psychological

experience and an extremely rewarding experience resulting from engagement in a variety of activities,

such as sports, writing, work, games and hobbies (Hsu et al., 2013). When an individual is in the flow

state, he/she becomes entirely focused on their activity while experiencing many positive experiential

characteristics, including great enjoyment and loss of self-consciousness. The Flow literatures have

been used to describe and model the individual experience in IT use, and occur in the presence of

important facilitators such as perceived control, personal enjoyment, and satisfaction. Kamis et al.

(2010) pointed out joyful from the on-line shopping can influence user satisfaction. Chou and Ting

(2003) thought while in the flow state, a consumer experiences a sense of happiness, accompanied by

a feeling of confidence and an exploratory desire. And, they considered that flow experience, the

emotional state embracing perceptional distortion and enjoyment, shows a much stronger impact on

addiction.

3 RESEARCH MODEL AND HYPOTHESIS

3.1 Case Description

There is a popular mobile shopping website, Rakuten Ichiba in Taiwan. The Rakuten Ichiba was born

in 2007. They had cash flow and logistics of President Chain Store Corporation (7-ELEVEn, Inc.), and

provided some services about on-line shopping including home delivery service and a pay-at-pickup

service at convenience stores. The Rakuten Ichiba developed a mobile shopping website in 2010, and

had accomplished 20% of overall sales from the mobile shopping website, and excepted to grown up

50% in this year (The Rakuten Ichiba in Taiwan, 2014). Thus, we focus on this successful the mobile

shopping website.

3.2 Research Model and Hypothesis

Tinsley (2000) indicated that P-E fit is an important theoretical construct in vocational,

industrial/organizational, and management psychology. P-E fit is used to understand the degree of fit

between a person and environments. In the context of continuance intention, Li (2013) pointed out that

an individual’s affective reaction toward information system acceptance cannot be neglected. At

video-game study, affective reaction which brings positive or negative reaction is based on their

performance (Bandura & Locke, 2003). Chang and Wang (2008) found that in the flow state, people

become absorbed in their activity: their perceptions are narrowed to the activity itself, they lose self-

consciousness, and they feel in control of their environment. Flow in the human-computer interaction

is a very interesting and exploratory experience. The system should be designed for providing more

users’ control force and concentration, and it can lead more intentions to using system (Webster et al.,

1993). According previously researches pointed out 40% of Internet users had a state of flow on the

Internet (Chen & Nilan, 1999). The flow is critical factor to influence people to use information

technology. Thus, relationships among value-technology fit, affective reaction, and continuance

intention to use the mobile shopping website are under investigated in this study. Fig.1. shows our

research model for a mobile shopping website.

V-T Fit CI

Flow

AFF

H1

H3

H2

H4

H5

V-T Fit: Value-technology fit; AFF: Affective Reaction; CI: Continuance Intention to use

Figure 1. The Research Model for a mobile shopping website

At salary job researches, there was confirmed when fitting better between employ’s characters and

work environment, it could influences affective reaction. The degree of this P-E fit could explain

additional variance in affective work outcomes above and beyond individual and environmental

factors (Vianen, 2005). Koo et al. (2006) found that the sponsoring events exhibiting a good image fit

with the brand can strengthen the development of consumers' cognitive and affective responses

through exposure to a brand's sport sponsorship activity. In the context of a mobile shopping website

environment, the Rakuten Ichiba provided several discounts/bargains in the mobile shopping website.

In this study, when consumers use discounts/bargains on a mobile shopping website, the Rakuten

Ichiba, they could have a state of attractive or friendly reaction for this website. Thus, the following

hypothesis is proposed:

H1: Value-Technology fit positively affects affective reaction of using the mobile shopping

website.

Aaker and Lee (2006) believed that regulatory fit that could shed light on domains important to

individual well-being (e.g., improving health) and provided a stronger theoretical grounding for extant

work (e.g., flow experiences). They found enhances the ‘feeling-right’ experience as well as strength

of engagement, and that the ‘feeling-right’ is just called flow. Regulatory fit has the strongest effect on

flow experience. In the context of the mobile shopping website environment, the Rakuten Ichiba

provided several discounts/bargains in the mobile shopping website. When consumers use

discounts/bargains on a mobile shopping website, the Rakuten Ichiba, they could have a state of

feeling-right for this website. Thus, the following hypothesis is proposed:

H2: Value-Technology fit positively affects flow experience of using the mobile shopping

website.

Junglas et al. (2009) found the relationship between human drives and fit types affects on determining

their impact on performance and utilization decisions for mobile information communication

technologies (MICTs). Lin (2012) indicated that perceived fit and satisfaction are important

precedents of the intention to continue virtual learning system (VLS) and individual performance. In

the context of the mobile shopping website environment, when consumers use discounts/bargains on a

mobile shopping website, the Rakuten Ichiba, they could feel happy for this website and continue to

use the mobile shopping website. Thus, the following hypothesis is proposed:

H3: Value-Technology fit positively affects continuance intention to use the mobile shopping

website.

Lee and Kwon’s (2011) results showed that continuance intention is affected conjointly by affective

factors (e.g. familiarity and intimacy). And the effects of affective factors (e.g. intimacy) were larger

than those of cognitive factors (e.g. perceived usefulness). They found that relationships between

consumers and web-based services which have been built up due to repetitive usage. Both affective

and cognitive factors explain consumers’ continuance intention. Finally, they considered the effects of

affective factors such as intimacy were larger than those of cognitive factors such as perceived

usefulness. Moreover, Koo et al. (2006) found the effects of consumers’ cognitive and affective

responses were significant in relation to intentions to purchase sport equipment in online website.

Therefore, the following hypothesizes is proposed:

H4: Affective reaction positively affects continuance intention to use the mobile shopping

website.

Flow has become popular construct, studied in the context of IT, and found to be useful in

understanding consumer behavior (Chang & Wang, 2008). For example, Novak and Kohler (1998)

applied Flow Theory to the comprehending customer navigation behavior in online environments.

Webster and Trevino (1995) studied the subjective human experience of interacting with the

technologies of electronic mail and voice mail. They defined the core flow experience of interacting

with technology. The four characteristics of this core flow experience are: feeling in control, focusing

attention on the activity, feeling curiosity, and being intrinsically interested. Flow affects all of these

outcome measures and is an important factor for understanding consumer behavior in commercial

Web sites (Hoffman & Novak, 1996). Hsu et al. (2012) pointed out that flow experience positively and

significantly influenced the continuance intention of shopping behavior. Zhou (2013) confirmed that

flow is positively related to continuance intention of mobile payment services. Moreover, Chang

(2013) also showed that users who have better flow experiences are more likely to have continuance

intentions of using Social network sites games (SNGs). Therefore, the following hypothesizes is

proposed:

H5: Flow positively affects continuance intention to use the mobile shopping website.

4 RESEARCH METHODOLOGY

4.1 Subjects

The participants were collected from a online survey via PTT (http://www.ptt.cc) and Rakuten Ichiba

in Taiwan. The participants were asked to fill in their e-mail address in order to avoid repeating

responses. A total 137 questionnaires were collected. To ensure valid samples, the study only retained

questionnaires from those who had used a mobile shopping website, the Rakuten Ichiba. There were

23 invalid questionnaires due to no experience on a mobile shopping website, the Rakuten Ichiba, with

114 usable questionnaires.

4.2 Measurement Development

Value-technology fit refers to benefits of matching with discounts/bargains and the mobile shopping

website. Flow refers to a state of mind experienced by consumers who are deeply involved in the

mobile shopping website (Novak et al., 2000). Affective reaction refers to a set of mental processes

including emotion, moods, and (possibly) attitudes when consumers use a mobile shopping website

(Bagozzi et al., 1999). Continuance intention refers to an individual’s subjective likelihood of

continuing to use the mobile shopping website (Jin et al., 2010). All of scale items were adapted from

previous literature except for value-technology fit, which is a self-development. All items were

measured on a five-point Likert scale, ranging from 1 point (strongly disagree) to 5 point (strongly

agree). Flow was adapted from Novak et al. (2000), and Chang and Wang (2008). Affective reaction

was adapted from Edell and Burke (1987). Finally continuance intention was adapted from Park et al.

(2012) and Yang (2012). The constructs and their items are shown in Table 1.

Construct Item Reference

Value-

Technology

fit

When I look for discounts, this mobile shopping website is matched. self-development

When I look for bargain goods, this mobile shopping website is

matched.

When I look for low-priced products, this mobile shopping website is

matched.

When I find the best product for my money, this mobile shopping

website is matched.

When I compare goods to find the best product for my money, this

mobile shopping website is match.

When I look for the store for sales, this mobile shopping website is

matched.

When I look for the store to find discounts, this mobile shopping

website is matched.

Affective

Reaction

This website is attractive. Edell and Burke

(1987) This website is cool.

This website is friendly.

This website is pleased.

Flow

I think I have ever experience flow on the mobile shopping website. Novak et al. (2000)

Chang and Wang

(2008) Most of the time I use the mobile shopping website I feel that I am in

flow.

I focus when I am using searching tools on the mobile shopping

website.

I feel perceived control when I am using searching tools on the

mobile shopping website.

Continuance

Intention to

use

I am going to positively utilize the mobile shopping website. Park et al. (2012)

Yang (2012) I have continuing concern about discounts of information to use the

mobile shopping website.

I expect this mobile shopping website to continue in the future.

I intend to purchase products or services via mobile phone.

Table 1. The constructs and their items

4.3 Non-response Bias Analysis

Armstrong and Overton (1977) indicated that if persons who respond differ substantially from those

who do not, the results do not directly allow one to say how the entire sample would have responded.

They suggested that it can be divided the period of survey into early stage and later stage. There were

114 usable questionnaires in this study. A total of 68 respondents who completed the survey during the

early stage were considered earlier respondents and 46 respondents completed the survey during the

later stage. The results of the Chi-square test for the early and late respondents shows they did not

differ significantly (p>.05) in age and education (see Table 2). Therefore, Non-response bias is not a

serious problem in this study.

Measure Group Sample Chi-square P-value

Age Early period 68

6.354 0.174 Later period 46

Educaton Early period 68

1.264 0.738 Later period 46

Table 2. Chi-square of Respondent Characteristics

4.4 Common Method Bias Analysis

When one factor which can explains common variances of total items was more than 50%, then there

was common method bias among variances (Harman, 1976). We assessed the data set using Harmon’s

one-factor test to identify any common method bias. A principal component factor analysis was

performed and the results excluded the potential threat of common methods bias. The combined six

factors accounted for 71.4% of total variance; the first (largest) factor accounted for 49.7% (the

variances explained ranges from 5.54% to 49.71%) and no general factor accounted for more than

50% of variance, indicating that common method bias may not be a serious problem in this study.

5 DATA ANALYSIS AND RESULTS

The data were analysed using IBM SPSS Statistics 19th and SmartPLS 2.0. The SPSS is used to show

demographic statistics. The partial least squares (PLS) is used to examine the measurement model

with reliability, convergent, and discriminant validity. PLS is also used to examine the structural

model to measure the strength of relationships among the theoretical constructs.

5.1 Demographic Profiles

Descriptive information of the sample for this research indicated that the 114 participants, 43% (n=49)

were male, and 57% (n=65) were female. Most of the respondents were college students (62.3%, n=71)

and between 19 and 25 years of age (47.4%, n=54). Most of respondents’ salaries were $18,000 or less

(36.8%, n=42). Descriptive information is shown in Table 3.

Measure Item Frequencies Percent (%)

Sex Male 49 43

Female 65 57

Age

<= 18 years old 6 5.2

19-25 years old 54 47.4

26-35 years old 45 39.5

36-45 years old 5 4.4

>=46 years old 4 3.5

Education

< High school 5 4.4

College (or Vocational) 71 62.3

Master 34 29.8

doctorate 4 3.5

Salary

<= $18,000 42 36.8

$18,001-25,000 14 12.3

$25,001-35,000 30 26.3

$35,001-45,000 18 15.8

>= $45,001 10 8.8

Time to use

the mobile

shopping

website*

1 -2 months 37 32.5

2 -5 months 39 34.2

5 -6 months 11 9.6

6 -7 months 10 8.8

> 7 months 17 14.9

*Mobile shopping: a mobile shopping website of the Rakuten Ichiba in Taiwan

Table 3. Descriptive Statistics of Respondent Characteristics (n=114)

5.2 Measurement Model

According to criteria of Petter et al. (2007): (a) direction of causality is from construct to items (b)

indicators are interchangeable (c) indicators covary with each other (d) nomological net for the

indicators are not differ, our research constructs are modelled as reflective ones. The measurement

model assesses the reliability and validity of this study. Three criteria suggested by Fornell and

Larcker (1981) were used to evaluate the measurement scales as follows. (1) The factor loading of all

items on its construct should exceed 0.7, (2) composite reliabilities (CR) should exceed 0.8, and (3)

average variance extracted (AVE) of each construct should exceed 0.5. In this study, most of the

indices are fit for criteria except for measurement item CI2 (see Table 4 and Table 5). The purpose of

discriminant validity is to discriminate the correlate from latent variables to others. The square root of

AVE for each construct should be greater than the correlation between others. In this study, the

discriminant validity also meets the requirement of recommended values shown in Table 6.

Construct Item Item Mean St. dev. Factor

Loading

Composite

Reliability AVE

Value-

Technology

fit

V-T Fit1 3.72 0.80 0.87

0.94 0.71

V-T Fit2 3.72 0.79 0.84

V-T Fit 3 3.72 0.80 0.91

V-T Fit 4 3.76 0.83 0.87

V-T Fit 5 3.73 0.89 0.81

V-T Fit 6 3.75 0.86 0.77

V-T Fit 7 3.61 0.81 0.82

Affective

Reaction

AFF1 3.71 0.74 0.83

0.90 0.70 AFF2 3.25 0.82 0.84

AFF3 3.6 0.83 0.81

AFF4 3.82 0.71 0.86

Flow FLOW1 3.5 0.85 0.84 0.90 0.70

FLOW2 3.62 0.91 0.88

FLOW3 3.66 0.87 0.83

FLOW4 3.75 0.83 0.79

Continuance

Intention to

use

CI1 3.5 0.85 0.86

0.87 0.64 CI2 3.4 1.10 0.53

CI3 3.78 0.82 0.90

CI4 3.96 0.71 0.86

Table 4. Scale Properties of Measurement Model

V-T Fit AFF FLOW CI

V-T Fit1 0.87 0.57 0.47 0.59

V-T Fit2 0.84 0.51 0.42 0.48

V-T Fit3 0.91 0.54 0.40 0.60

V-T Fit4 0.87 0.58 0.42 0.55

V-T Fit5 0.81 0.55 0.47 0.59

V-T Fit6 0.77 0.52 0.45 0.63

V-T Fit7 0.82 0.48 0.49 0.52

AFF1 0.57 0.83 0.59 0.62

AFF2 0.47 0.84 0.36 0.53

AFF3 0.52 0.81 0.40 0.57

AFF4 0.56 0.86 0.48 0.54

FLOW1 0.45 0.43 0.84 0.54

FLOW2 0.45 0.51 0.88 0.53

FLOW3 0.42 0.41 0.83 0.52

FLOW4 0.45 0.50 0.79 0.50

CI1 0.60 0.69 0.56 0.86

CI2 0.32 0.32 0.29 0.53

CI3 0.64 0.56 0.54 0.90

CI4 0.54 0.53 0.58 0.86

* V-T Fit: Value-technology fit; AFF: Affective Reaction; CI: Continuance Intention to use

Table 5. Cross Loadings

Construct* V-T Fit AFF FLOW CI

V-T Fit 0.84

AFF 0.64 0.84

FLOW 0.53 0.55 0.84

CI 0.67 0.68 0.63 0.80

* V-T Fit: Value-technology fit; AFF: Affective Reaction; CI: Continuance Intention to use

Table 6. Discriminant Validity

5.3 Structural Model

The results of the structural model are shown in Fig.2. In structural model, it is important to determine

the significance and strength of coefficient path and the variance explained ( value). The

examination of significance used the bootstrap procedure (5000 resample) of PLS. All of the

hypothesized paths are significant and supported in this study.

V-T FitCI

Flow

AFFH1

0.637***

(t=9.526)

H5

0.282**

(t=3.055)

H2

0.531***

(t=7.827)

H3

0.319*

(t=2.571)

H4

0.319**

(t=2.795)

=0.608

R2=0.282

=0.406

R2

R2

*p<0.05; **p<0.01; ***p<0.001

V-T Fit: Value-technology fit; AFF: Affective Reaction; CI: Continuance Intention to use

Figure 2. Results of Structural Model

The model explains 60.8% ( =0.608) of the variance in continuance intention to use the mobile

shopping website; 28.2% ( =0.282) of the variance in flow; and 40.6% ( =0.406) of the variance in

affective reaction. Hypothesis 1 examines the effects of value-technology fit on affective reaction.

Value-technology fit was significantly related to affective reaction (γ=0.637, p<0.001), supporting H1.

Hypothesis 2 examines the effects of value-technology fit on flow. Value-technology fit was

significantly related to flow (γ=0.531, p<0.001), supporting H2. Hypothesis 3 examines the effects of

value-technology fit on continuance intention to use the mobile shopping website. Value-technology

fit was significantly related to continuance intention to use the mobile shopping website (γ=0.319,

p<0.05), supporting H3. Hypothesis 4 examines the effects of affective reaction on continuance

intention to use the mobile shopping website. Affective reaction was significantly related to

continuance intention to use the mobile shopping website (γ=0.319, p<0.01), supporting H4.

Hypothesis 5 examines the effects of flow on continuance intention to use the mobile shopping

website. Flow was significantly related to continuance intention to use the mobile shopping website

(γ=0.282, p<0.01), supporting H5.

To evaluate mediating effect of affective reaction and flow, Esposito Vinzi et al. (2010) pointed out

that the variance accounted for (VAF) is a good criteria: the no mediation is less than 20%, the partial

mediation is between 20% and 80%, and the full mediation is exceed 80%. Hair et al. (2013) showed

that the VAF determined the size of the indirect effect in relation to the total effect, and was explained

how much of the target construct’s variance was explained by the indirect relationship via the

mediator variable. The VAF of affective reaction and flow in this study are 40% and 30%. Therefore

the affective reaction and flow had a partial mediation between value-technology fit and continuance

intention in the model.

6 DISCUSSION AND CONCLUSION

This research focuses on the effects of value-technology fit, affective reaction, and flow on

continuance intention to use the mobile shopping website. The results of this study provide support for

the research model and all of the coefficient paths are significant. Our result is consistent with Koo et

al.’s that fit positively influences affective reaction of using a mobile shopping website (Koo et al.,

2006). The result indicates that the newly proposed construct, value-technology fit of the mobile

shopping website, is significant for all respondents. While users believe matching with

discounts/bargains and the mobile shopping website, they probably get positive feel, like, happy and

comfortable. The value-technology fit has a significant effect on flow of using the mobile shopping

website. The result of this study is consistent with Aaker and Lee‘s that fit had positively significant

on flow (Aaker & Lee, 2006). The mobile shopping websites provide a lot of useful discounts for

what they need which could make users begin to use the mobile shopping website. The value-

technology fit has a significant effect on continuance intention to use the mobile shopping website.

The result of this study is consistent with Lin’s that fit had positively significant on the continuance

intention to use (Lin, 2012). While users believe matching with discounts/bargains on the mobile

shopping website, they would probably continue using the mobile shopping website. The affective

reaction has a significant effect on continuance intention to use the mobile shopping website. The

result of this study is consistent with Lee and Kwon’s that affective reaction had positively significant

on continuance intention to use (Lee & Kwon, 2011). While users have a positive feeling to use the

mobile shopping website, they will high probability to continue using the mobile shopping website.

The flow has a significant effect on continuance intention to use the mobile shopping website. The

result of this study is consistent with Chang and Wang’s that flow had positively significant on

continuance intention to use (Chang & Wang, 2008). While users have a state of flow in the mobile

shopping website, they would probably continue using the mobile shopping website. Based on

mediation test, the affective reaction and flow had a partial mediation in the model. While users have a

state of positive affective or flow when searching discounts, they will have more continuance intention

to use the mobile shopping website.

The purpose of this study is to study the factors (value-technology fit, affective reaction, and flow)

influencing continuance intention to use the mobile shopping website. Empirical data for this study is

collected via online survey. Structural equation model (SEM) is used to examine hypotheses and

variance explanation. Our results show that users’ value-technology fit affects their affective reaction,

flow, and continuance intention to use the mobile shopping website. Affective reaction and flow are

important mediators between value-technology fit and continuance intention to use the mobile

shopping website. The major contribution of this study is to explain well users’ continuance intention

of the mobile shopping website by value-technology fit, affective reaction, and flow. The findings of

this research suggest that these mobile shopping managers should pay attention to the factors which

can increase fit between value and technology, the users’ sense of affective reaction and flow, and

offer more benefits in using a mobile shopping website that increase their competitive advantage in

mobile business.

7 LIMITATIONS

This study suffers from three main limitations. First, the subjects are customers who had using

experiences on a mobile shopping, the Rakuten Ichiba in Taiwan. Therefore, the generalization of the

results to other mobile shopping should be cautious. Second, this study considered limited factors

influencing continuance intention to use a mobile shopping website. Future studies should explore

other variables of advantages on mobile commerce, which probably have effects on users’ continuance

intention of using the mobile shopping. Finally, this study only tested common variances of total items

with Harmon’s one-factor for CMB. Future studies may consider other methods for CMB. Although

there are limitations, this study has important implications for managers of mobile shopping

companies, and academics. From the practical perspective, the findings of this research help these

mobile shopping managers to better understand users’ continuance intention on a mobile shopping

website. From the academic perspective, this research finds that value-technology fit is an important

factor influencing consumers’ continuance intention to use a mobile shopping website. This study also

shows that affective reaction and flow are important partial mediators. To enhance the effects of

affective reaction and flow may strength continuance intention to use a mobile shopping website.

ACKNOWLEDGEMENT

This study was supported by the National Science Council (Project Number: NSC 102-2410-H-130 -

038).

References

Aaker, J. L., and Lee, A. Y. (2006). Understanding regulatory fit. Journal of Marketing Research

(JMR), 43(1), 15-19.

Aljukhadar, M., Senecal, S., and Nantel, J. (2014). Is more always better? Investigating the task-

technology fit theory in an online user context. Information & Management, 51(4), 391-397.

Armstrong, J. S., and Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of

Marketing Research (JMR), 14(3), 396-402.

Bagozzi, R. P., Gopinath, M., and Nyer, P. U. (1999). The Role of emotions in marketing. Journal of

the Academy of Marketing Science, 27(2), 184-206.

Bandura, A., and Locke, E. A. (2003). Negative self-efficacy and goal effects revisited. Journal of

Applied Psychology, 88(1), 87-99.

Chang, C. C. (2013). Examining users' intention to continue using social network games: A flow

experience perspective. Telematics and Informatics, 30(4), 311-321.

Chang, H. H., and Wang, I. C. (2008). An investigation of user communication behavior in computer

mediated environments. Computers in Human Behavior, 24(5), 2336-2356.

Chen, H., and Nilan, M. (1999). On-line ESM data collection tool – transformation, implementation,

and assessment. In Proceedings of the 5th America's conference on information systems,

Milwaukee, WI.

Chou, T. J., and Ting, C. C. (2003). The role of flow experience in cyber-game addiction.

Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on

behavior and society, 6(6), 663-675.

Cooper, C. L., Dewe, P. J., and O' Driscoll, M. P. (2001). Organizational stress. Thousand Oaks, CA:

Sage Publications.

Csikszentmihalyi, M. (1993). The evolving self. New York: Harper & Row.

Djamasbi, S., and Strong, D. M. (2008). The effect of positive mood on intention to use computerized

decision aids. Information & Management, 45(1), 43-51.

Dusick, D. M. (1998). What social cognitive factors influence faculty members' use of computers for

teaching? A literature review. Journal of Research on Computing in Education, 31(2), 123.

Edell, J. A., and Burke, M. C. (1987). The power of feelings in understanding advertising effects.

Journal of Consumer Research, 14(3), 421-433.

Esposito Vinzi, V., Chin, W. W., Henseler, J., and Wang, H. (2010). Handbook of partial least squares:

concepts, methods and applications: Springer.

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Gonsalves, A. (2009). One billion mobile internet devices seen by 2013. From

http://www.informationweek.com/news/internet/webdev/222001329

Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2013). A primer on partial least squares

structural equation modeling (PLS-SEM): Sage Pubns.

Harman, H. H. (1976). Modern factor analysis: University of Chicago Press.

Hoffman, D. L., and Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments:

Conceptual foundations. Journal of Marketing, 60(3), 50.

Hsu, C. L., Chang, K. C., and Chen, M. C. (2012). Flow experience and internet shopping behavior:

Investigating the moderating effect of consumer characteristics. Systems Research and

Behavioral Science, 29(3), 317-332.

Hsu, C. L., Wu, C. C., and Chen, M. C. (2013). An empirical analysis of the antecedents of e-

satisfaction and e-loyalty: Focusing on the role of flow and its antecedents. Information

Systems & e-Business Management, 11(2), 287-311.

Hu, C., Su, H. C., and Chen, C.-I. B. (2007). The effect of person–organization fit feedback via

recruitment web sites on applicant attraction. Computers in Human Behavior, 23(5), 2509-

2523.

Jin, X. L., Lee, M. K. O., and Cheung, C. M. K. (2010). Predicting continuance in online communities:

Model development and empirical test. Behaviour & Information Technology, 29(4), 383-394.

Jr., R. D. B., Rynes, S. L., and Gerhart, B. (1993). Recruiter perceptions of applicant fit: Implications

for individual career preparation and job search behavior. Journal of Vocational Behavior,

43(3), 310-327.

Junglas, I., Abraham, C., and Ives, B. (2009). Mobile technology at the frontlines of patient care:

Understanding fit and human drives in utilization decisions and performance. Decision

Support Systems, 46(3), 634-647.

Kamis, A., Stern, T., and Ladik, D. M. (2010). A flow-based model of web site intentions when users

customize products in business-to-consumer electronic commerce. Information Systems

Frontiers, 12(2), 157-168.

Kaplan, J. D. (1950). Dialogues of Plato. New York: Washington Square Press.

Karaatli, G., Jun, M., and Suntornpithug, N. (2010). 'Investigating mobile services' impact on

consumer shopping experience and consumer decision-making. International Journal of

Mobile Marketing, 5(2), 75-86.

Ko, E., Kim, E. Y., and Lee, E. K. (2009). Modeling consumer adoption of mobile shopping for

fashion products in Korea. Psychology & Marketing, 26(7), 669-687.

Koo, G. Y., Quarterman, J., and Flynn, L. (2006). Effect of perceived sport event and sponsor image

fit on consumers' cognition, affect, and behavioral intentions. 15, 80-90.

Kristof-Brown, A. L., Zimmerman, R. D., and Johnson, E. C. (2005). Consequences of individuals' fit

at work: A meta-analysis of person-job, person-organization, person-group, and person-

supervisor fit. Personnel Psychology, 58(2), 281-342.

Kumar, A., and Mukherjee, A. (2013). Shop while you talk: Determinants of purchase intentions

through a mobile device. International Journal of Mobile Marketing, 8(1), 23-37.

Lee, I., Choi, B., Kim, J., and Hong, S. J. (2007). Culture-Technology Fit: Effects of Cultural

Characteristics on the Post-Adoption Beliefs of Mobile Internet Users. International Journal of

Electronic Commerce, 11(4), 11-51.

Lee, Y., and Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended

expectation–confirmation model in web-based services. Electronic Commerce Research and

Applications, 10(3), 342-357.

LeRouge, C., Nelson, A., and Blanton, J. E. (2006). The impact of role stress fit and self-esteem on the

job attitudes of IT professionals. Information & Management, 43(8), 928-938.

Li, C. Y. (2013). Persuasive messages on information system acceptance: A theoretical extension of

elaboration likelihood model and social influence theory. Computers in Human Behavior,

29(1), 264-275.

Lin, W. S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance

intention and task-technology fit perspectives. International Journal of Human-Computer

Studies, 70(7), 498-507.

Lu, H. P., and Yang, Y. W. (2014). Toward an understanding of the behavioral intention to use a

social networking site: An extension of task-technology fit to social-technology fit. Computers

in Human Behavior, 34(0), 323-332.

Market Intelligence and Consulting Institute, M. (2013). 57 percent of consumers had experiences of

shooping with mobile devices in Taiwan. From

http://www.bnext.com.tw/article/view/id/28330

Maurer, S. D., and Cook, D. P. (2011). Using company web sites to e-recruit qualified applicants: A

job marketing based review of theory-based research. Computers in Human Behavior, 27(1),

106-117.

Musil, S. (2013). Shopping via mobile devices increased 81 percent in 2012. From

http://news.cnet.com/8301-1023_3-57563184-93/shopping-via-mobile-devices-increased-81-

percent-in-2012/

Ngai, E. W. T., and Gunasekaran, A. (2007). A review for mobile commerce research and applications.

Decision Support Systems, 43(1), 3-15.

Novak, T., and Kohler, J. L. (1998). Technological innovations in deep coal mine power systems.

Industry Applications, IEEE Transactions on, 34(1), 196-204.

Novak, T. P., Hoffman, D. L., and Yiu-Fai, Y. (2000). Measuring the customer experience in online

environments: A structural modeling approach. Marketing Science, 19(1), 22.

Okazaki, S., and Yagüe, M. J. (2012). Responses to an advergaming campaign on a mobile social

networking site: An initial research report. Computers in Human Behavior, 28(1), 78-86.

Ozok, A. A., and Wei, J. (2010). An empirical comparison of consumer usability preferences in online

shopping using stationary and mobile devices: Results from a college student population.

Electronic Commerce Research, 10(2), 111-137.

Park, S. Y., Nam, M. W., and Cha, S. B. (2012). University students' behavioral intention to use

mobile learning: Evaluating the technology acceptance model. British Journal of Educational

Technology, 43(4), 592-605.

Pervin, L. A. (1968). Performance and satisfaction as a function of individual-environment fit.

Psychological Bulletin, 69(1), 56-68.

Petter, S., Straub, D., and Rai, A. (2007). Specifying formative constructs in information systems

research. MIS Quarterly, 31(4), 623-656.

Ping, Z. (2013). The affective response model: A theoretical framework of affective concepts and their

relationships in the ICT context. MIS Quarterly, 37(1), 247-274.

Taiwan Network Information Center, T. (2012). A survey on broadband usage in Taiwan. From

http://www.twnic.net.tw/download/200307/20120709a.pdf

The Rakuten Ichiba in Taiwan. (2014). The Rakuten release lucky bear photograph of LINE and

master mobile shopping opportunities. From

http://www.rakuten.com.tw/info/release/2014/0107.html

Tinsley, H. E. A. (2000). The congruence myth: An analysis of the efficacy of the person–environment

fit model. Journal of Vocational Behavior, 56(2), 147-179.

Turban, E., Chung, H. M., Lee, J. K., and Chung, M. (2008). Electronic commerce: A managerial

perspective 5/e: Prentice Hall.

Vaidya, S. D., and Seetharaman, P. (2011). Explaining Sophistication in Collaborative Technology

Use: A Context-Technology Fit Perspective. Group Decision and Negotiation, 20(2), 185-213.

Vianen, A. E. M. v. (2005). A review of person-environment fit research: Prospects for personnel

selection.

Webster, J., and Trevino, L. K. (1995). Rational and social theories as complementary explanations of

communication media choices: Two policy-capturing studies. Academy of Management

Journal, 38(6), 1544-1572.

Webster, J., Trevino, L. K., and Ryan, L. (1993). The dimensionality and correlates of flow in human-

computer interactions. Computers in Human Behavior, 9(4), 411-426.

Wu, J. H., Chen, Y. C., and Lin, L. M. (2007). Empirical evaluation of the revised end user computing

acceptance model. Computers in Human Behavior, 23(1), 162-174.

Yang, H. D., Kang, S., Oh, W., and Kim, M. S. (2013). Are All Fits Created Equal? A Nonlinear

Perspective on Task-Technology Fit. Journal of the Association for Information Systems,

14(12), 694-721.

Yang, K. (2012). Consumer technology traits in determining mobile shopping adoption: An

application of the extended theory of planned behavior. Journal of Retailing and Consumer

Services, 19(5), 484-491.

Zhou, T. (2013). An empirical examination of the determinants of mobile purchase. Personal and

Ubiquitous Computing, 17(1), 187-195.